Industry
Banking
Banking Services Transformation: Unlocking Exponential Value
Highlights
- Mortgage fraud has emerged as a serious problem given the huge number of loans, the large ticket size, and the growing sophistication of fraudsters.
- For lenders, the consequences of mortgage fraud include financial and reputation loss as well as regulatory scrutiny and penalties.
- Mortgage lenders must integrate GenAI tools across the loan lifecycle to enable real-time fraud detection and prevention.
Overview
For financial institutions, mortgage lending has traditionally been a profitable line of business.
However, given the huge number of loans involved, it is a lucrative target for fraudsters. With growing digitization and fraudsters ramping up the use of sophisticated tools, mortgage fraud is fast emerging as a serious concern. A failure to mitigate it can result in financial loss, reputation loss, and regulatory penalties for mortgage lenders. However, successful prevention of mortgage fraud necessitates detection before it materializes. Consequently, real-time fraud detection and prevention have become top priorities for mortgage lenders.
The mortgage lifecycle, starting from application submission to processing to underwriting and ultimately, disbursement of funds and post-sanction services, is highly vulnerable to fraud, including identity theft, income misrepresentation, property overvaluation, and fictitious borrowers. Preventing these types of fraud mandates detection in real time, a capability that traditional systems aren’t equipped with as they rely on rule-based engines and retrospective audits.
Generative artificial intelligence (GenAI) offers a transformative opportunity for banks and mortgage providers to proactively detect, interpret, and prevent fraud throughout the mortgage lifecycle. GenAI systems can rapidly analyze huge amounts of data and enable a steady flow of intelligence, which can prove vital in fraud prevention.
Common types of mortgage fraud
Both borrowers and bank employees can exploit vulnerabilities across the mortgage lending lifecycle to commit fraud.
Let us examine the most frequent manifestations of mortgage fraud.
Income misrepresentation: Borrowers, at times with the help of third parties, falsify or exaggerate income details to meet the eligibility criteria. Common tactics include fabricating pay slips, manipulating tax returns, and submitting fictitious employment details.
Occupancy: Prospective borrowers claim the property in question to be their primary residence (when in reality, it may be intended for rental or resales purposes) in order to benefit from better terms applicable to loans meant for self-occupied properties. In such scenarios, mortgage lenders incur losses as the loan is sanctioned at a relatively lower rate of interest.
Identity theft: Fraudsters use stolen or fictitious identities to apply for mortgage loans, typically with no intention of repayment. This often results in significant losses once discovered.
Property appraisal: Prospective borrowers deliberately inflate the market value of the property in question, typically through collusion with appraisers and sellers, making the loan size bigger than what the property warrants. Now, if the borrower defaults, the lender will not be able to recover the full amount of the loan by selling the property.
Undisclosed obligations: Prospective borrowers deliberately conceal existing financial liabilities to bypass the debt-to-income ratio checks, which are part of the credit assessment performed by mortgage lenders. Consequently, they qualify for loans they would otherwise not be eligible for.
Wire and title fraud: Cybercriminals intercept wire transfer instructions, alter them, and divert funds to a different account. Title fraud involves illegal manipulation of property ownership records, often using forged documents. Both wire and title fraud result in significant monetary as well as reputational losses to mortgage lenders—during Q1 2025, on an $80 billion mortgage portfolio comprising residential, commercial, and business loans, 46.8% of transactions had issues with potential risk of wire and title fraud. In addition, problematic loans had 2.5 issues per loan on average.1
Conventional fraud detection systems typically rely on static rules and historical flags. These systems can be bypassed by sophisticated fraudsters and are therefore unable to detect the different types of fraud that mortgage lenders have to contend with. Existing systems fail to adapt to evolving fraud patterns. Additionally, they often function in silos, disconnected from contextual borrower insights or cross-platform activity, leading to blind spots in fraud identification. Moreover, mortgage fraud detection is typically retrospective—in-depth investigation is carried out when a fraud is suspected, by which time the fraud would have already occurred and the fallout may be too big to contain. This reactive model is no longer sustainable. Mortgage fraud has become more organized and intelligent, necessitating a shift to adaptive, self-learning technologies that can predict and prevent frauds.
Real-time fraud detection: Enter GenAI
For lenders, mortgage fraud is not only a compliance risk but also a direct threat to profitability.
Mortgage lenders lose billions of dollars annually due to undetected fraud, which invites regulatory penalties apart from negatively impacting loan performance and reputation. To retain market position and avoid adverse consequences, mortgage lenders must act quickly. Even a marginal improvement in fraud detection can minimize risks and prevent losses to the tune of millions of dollars.
We propose a GenAI backed cognitive copilot to assist human decision-makers. This hybrid model improves productivity and reduces burnout among fraud analysts and underwriters by facilitating a continuous flow of fraud insights while mitigating false alarms. It allows them to focus on complex or borderline cases that demand an analysis of complex human behavior amid changing conditions which an AI algorithm may not be able to perform. The blend of AI-driven insights and human judgment ensures a balanced approach to mortgage decision-making. Lenders can deploy GenAI tools at different stages of the mortgage cycle (see Figure 1).
Figure 1: GenAI interventions to detect mortgage fraud in real time
Application submission
During application submission, GenAI can help analyze data inputs and borrower profiles in real time. Leveraging behavioral profiling and anomaly detection, it creates dynamic risk profiles by scrutinizing borrower activity across multiple touchpoints. This proactive approach enables the identification of irregular patterns, such as inconsistencies in personal information or fabricated documentation, before they progress further in the process. GenAI’s ability to detect synthetic identities through cross-verification and linguistic analysis of application data ensures that fraudulent personas are flagged early, preventing them from advancing to subsequent stages.
Underwriting
In the underwriting phase, GenAI can augment traditional risk assessment methods by providing explainable risk scores and summarizing potential concerns. Drawing from vast datasets and historical fraud patterns, it identifies red flags that may not be immediately apparent through conventional analysis. Intelligent document analysis further supports this stage by verifying the authenticity of income documents, pay slips, and tax returns, reducing the possibility of accepting falsified financial documents. By serving as a cognitive co-pilot, GenAI allows human underwriters to focus on complex cases that require nuanced judgment, thereby enhancing productivity and reducing burnout.
Property appraisal
During property appraisal, using GenAI tools can help recognize irregular patterns in property valuation and transaction histories. By analyzing multi-dimensional data, it can identify discrepancies that may indicate inflated property values or manipulated sales data. This capability ensures that lenders have an accurate understanding of property value and are not misled by fraudulent appraisals. GenAI’s insights enable early intervention, preventing potential losses associated with overvalued properties.
Closing
At the closure stage, GenAI tools can help validate documentation and final transaction details. Intelligent document analysis ensures that all legal and financial documents are authentic and consistent with previously submitted information. This thorough examination reduces the risk of last-minute fraudulent activity, ensuring the integrity of the loan approval process. AI-driven insights help human decision-makers ensure that all aspects of the transaction are legitimate, facilitating smooth closure.
Post-sanction servicing
Even after loan disbursement, GenAI tools can be leveraged for post-sanction servicing and scrutiny by continuously monitoring borrower behaviour and financial activity. Actions such as luxury purchases can signal falsification of income details. Similarly, a borrower may apply for a second mortgage with another lender. Ongoing surveillance using GenAI allows the detection of such activities as well as emerging fraud patterns and anomalies that may develop over time. By learning from new trends and adapting its models, GenAI helps identify suspicious activity, ensuring long-term protection against fraud.
The way forward
Mortgage fraud detection must evolve to keep pace with the increasing sophistication of fraudsters.
Investment in emerging disruptive technologies such as GenAI is vital to enhance fraud resilience and foster trust among regulators and customers alike.
The future of mortgage fraud prevention demands a proactive and adaptive defense mechanism underpinned by AI tools and techniques. Mortgage lenders must focus on augmenting their fraud-fighting arsenal with GenAI tools. This may necessitate working with an IT transformation partner with the requisite domain expertise and implementation experience post a well-rounded market analysis.
References
1 FundingShield; FundingShield Wire Fraud Analytics Q1 2025; April 2025; Retrieved May 2025; https://www.fundingshield.com/fundingshield-q1-2025-fraud-analytics-with-commentary-from-fundingshields-ceo-ike-suri/
